18,991 research outputs found
A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics
The combination of multiple classifiers using ensemble methods is
increasingly important for making progress in a variety of difficult prediction
problems. We present a comparative analysis of several ensemble methods through
two case studies in genomics, namely the prediction of genetic interactions and
protein functions, to demonstrate their efficacy on real-world datasets and
draw useful conclusions about their behavior. These methods include simple
aggregation, meta-learning, cluster-based meta-learning, and ensemble selection
using heterogeneous classifiers trained on resampled data to improve the
diversity of their predictions. We present a detailed analysis of these methods
across 4 genomics datasets and find the best of these methods offer
statistically significant improvements over the state of the art in their
respective domains. In addition, we establish a novel connection between
ensemble selection and meta-learning, demonstrating how both of these disparate
methods establish a balance between ensemble diversity and performance.Comment: 10 pages, 3 figures, 8 tables, to appear in Proceedings of the 2013
International Conference on Data Minin
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models
Learning accurate probabilistic models from data is crucial in many practical
tasks in data mining. In this paper we present a new non-parametric calibration
method called \textit{ensemble of near isotonic regression} (ENIR). The method
can be considered as an extension of BBQ, a recently proposed calibration
method, as well as the commonly used calibration method based on isotonic
regression. ENIR is designed to address the key limitation of isotonic
regression which is the monotonicity assumption of the predictions. Similar to
BBQ, the method post-processes the output of a binary classifier to obtain
calibrated probabilities. Thus it can be combined with many existing
classification models. We demonstrate the performance of ENIR on synthetic and
real datasets for the commonly used binary classification models. Experimental
results show that the method outperforms several common binary classifier
calibration methods. In particular on the real data, ENIR commonly performs
statistically significantly better than the other methods, and never worse. It
is able to improve the calibration power of classifiers, while retaining their
discrimination power. The method is also computationally tractable for large
scale datasets, as it is time, where is the number of
samples
Doubly Optimized Calibrated Support Vector Machine (DOC-SVM): an algorithm for joint optimization of discrimination and calibration.
Historically, probabilistic models for decision support have focused on discrimination, e.g., minimizing the ranking error of predicted outcomes. Unfortunately, these models ignore another important aspect, calibration, which indicates the magnitude of correctness of model predictions. Using discrimination and calibration simultaneously can be helpful for many clinical decisions. We investigated tradeoffs between these goals, and developed a unified maximum-margin method to handle them jointly. Our approach called, Doubly Optimized Calibrated Support Vector Machine (DOC-SVM), concurrently optimizes two loss functions: the ridge regression loss and the hinge loss. Experiments using three breast cancer gene-expression datasets (i.e., GSE2034, GSE2990, and Chanrion's datasets) showed that our model generated more calibrated outputs when compared to other state-of-the-art models like Support Vector Machine (p=0.03, p=0.13, and p<0.001) and Logistic Regression (p=0.006, p=0.008, and p<0.001). DOC-SVM also demonstrated better discrimination (i.e., higher AUCs) when compared to Support Vector Machine (p=0.38, p=0.29, and p=0.047) and Logistic Regression (p=0.38, p=0.04, and p<0.0001). DOC-SVM produced a model that was better calibrated without sacrificing discrimination, and hence may be helpful in clinical decision making
- …